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Unsupervised semantic segmentation seeks to uncover and localize semantically significant categories within image corpora without any annotation. However, there are several challenges in creating annotated training data. These challenges frequently often outweigh semantic segmentation methods’ superior accuracy. Algorithms must develop features for every pixel that are both semantically relevant and compact enough to form discrete clusters to extract meaningful categories with any annotation from the training data. A team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Google, and Cornell University has achieved this by creating a machine learning model named STEGO (Self-supervised Transformer with Energy-based Graph Optimization) that surpasses previous methods by decoupling feature learning from cluster compactification.
A frozen backbone makes up STEGO, and it serves as a source of learning feedback and input to the segmentation head for predicting distilled characteristics. This segmentation head is a direct feed-forward network with a ReLU activation function. Unlike earlier studies, the algorithm’s efficiency was increased without retraining or fine-tuning the backbone. The STEGO neural network retrieves global image information by pooling spatial variables in a global average. Then, based on the cosine similarity in the backbone’s feature space, a lookup table is computed for each image’s K-Nearest Neighbours.
Continue Reading
Paper: https://arxiv.org/pdf/2203.08414.pdf
Github: https://github.com/mhamilton723/STEGO
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Dealing with a whopping amount of data is normal for businesses in any sector these days. Without using this information obtained from various sources, these entities find it hard to analyze various factors and make strategic decisions. The same can be said about the healthcare sector. Especially after the Covid-19 pandemic, the clinics and medical… Read More »How Power BI Applications Are Reshaping The Healthcare Industry
The post How Power BI Applications Are Reshaping The Healthcare Industry appeared first on Data Science Central.
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We are doing a survey related to multi-agent reinforcement learning systems and benchmarks and would love to hear your opinion.
This survey has 3 questions and will take about 10 seconds to complete.
We really appreciate your participation.
The survey URL is here.
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https://youtu.be/yi-A0kWXEO4
This video explains and summarizes the 87 pages long PaLM: Pathways Language Models paper from Google AI’s Pathways. Yes, it is that 540 billion dense parameter model which can explain jokes and is sensitive to chain of thought reasoning.
Paper link: https://arxiv.org/abs/2204.02311
PaLM blog post: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html
Outline:
00:00 DALL-E 2 or PaLM?
01:14 Weights&Biases (Sponsor)
02:25 A brief history of boring large language models
03:43 What is PaLM?
05:11 Training PaLM on all TPUs
08:11 PaLM training data
08:49 What it can do
10:31 Few-shot learning explained
13:20 Explaining jokes and Outlook
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Hi everyone,
I want to share with you an article that I worked on with my colleague about the use cases of human pose estimation. I would love it if you could check it out and share your ideas for using this technology in the comments below.
https://mobidev.biz/blog/human-pose-estimation-technology-guide
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I was reading this paper and in section 3 they claim that agents act simultaneously and there is no notion of turn-taking:
https://arxiv.org/abs/2104.07750
I was wondering how this works. What I'm used to seeing is a for loop in which, one after the other, agents execute the step function and interact with the environment. How does this change if all agents act simultaneously?
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Reinforcement learning (RL) is a machine learning training strategy that rewards desirable behaviors while penalizing undesirable ones. A reinforcement learning agent can perceive and comprehend its surroundings, act, and learn through trial and error in general. Although RL agents can heuristically solve some problems, such as assisting a robot in navigating to a specific location in a given environment, there is no guarantee that they will be able to handle problems in settings they have not yet encountered. The capacity of these models to recognize the robot and any obstacles in its path, but not changes in its surrounding environment that occur independently of the agent, which we refer to as exogenous noise, is critical to their success.
Existing RL algorithms are not powerful enoug…
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www.usage.ai
Usage AI bundles 3-year no-upfront RIs on AWS with guaranteed buyback -- so users get all the savings of 3-year RIs with none of the commitment. I helped engineer the product. Here to answer any questions!
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https://gradio.app/ is for demoing machine learning models
https://reddit.com/link/ueod3x/video/75e1ozmjihw81/player
Prerequisite: Python 3.7+ and that's it!
Quick Start
To get Gradio running with a simple "Hello, World" example, follow these three steps:
Install Gradio from pip.
pip install gradio
Run the code below as a Python script or in a Python notebook (or in a colab notebook).
import gradio as gr def greet(name): return "Hello " + name + "!!" demo = gr.Interface(fn=greet, inputs="text", outputs="text") if __name__ == "__main__": demo.launch()
The interface below will appear automatically within the Python notebook, or pop in a browser on http://localhost:7860 if running from a script.
see more in the getting started guide: https://gradio.app/getting_started/
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Hello everyone! Following this post numpy in fhe we are releasing a new lib that allows popular machine learning frameworks to run over encrypted data: https://github.com/zama-ai/concrete-ml
Currently this supports xgboost and many sklearn models. We also support pytorch to some extent.
We are trying to closely follow sklearn API (when relevant) to make the use easy to machine learning practitioners.
Happy to hear any feedback on this !
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Hey r/MachineLearning,
I'm Derrick from Layer (layer.ai) - the collaboration-first machine learning platform that enables you to build, train, track, and share your ML projects simply with a few lines of code.
We are soft-launching today! I’ve been working on Layer for the past 2 years with an awesome team around the world. We really poured our hearts and minds into Layer and hope you will like it. Your feedback would be very appreciated!
Layer Demo
To get started, you can simply run our Quickstart Example!
How is Layer different from other tools?
Although there are plenty of ML and DS tooling products, we believe that there is still a large gap around collaboration. Many data science projects are hosted on GitHub, which, in our experience, does not provide sufficient depth and abs…
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Their model’s predictions should help researchers improve ocean climate simulations and hone the design of offshore structures.
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"The Megalodon was a large bivalve, measuring up to 2.5 meters in length. Its shell was covered in spines, and it had a large, powerful jaw for crushing prey."
Although the megalodon is the most widely known as a giant prehistoric shark, I recently learned that Megalodon
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AI Weirdness: the strange side of machine learning
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Terraform Provider Iterative (TPI) address the specific needs of machine learning teams - it is an open-source tool extending the functionality of Terraform, the world's most widely used multi-cloud provisioning product. The tool enables full lifecycle management of computing resources and is designed specifically for machine learning pipelines: Terraform plugin for machine learning workloads: spot instance recovery & auto-termination | AWS, GCP, Azure, Kubernetes
The tool aims to bridge the gap between devops and data science teams and build on top of Terraform, a tool universally familiar to devops teams, but extend it to suit machine learning needs. It provides to following advantages for your ML workflow:
Lower cost: use your preferred cloud provider's existing pricing, including on-demand per-second billing and bulk discounts.
Auto-recovery: spot/preemptible instances are cheap but unreliable. TPI reliably and automatically respawns such interrupted instances, caching & restoring the working directory in the cloud even when you are offline.
Custom spec: full control over hardware & software requirements via a single config file.
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Hi folks, our blog post on how to automatically find label errors in audio datasets has just gone live. We cover the steps to:
⛏️ Perform feature extraction (aka embeddings) on the Spoken Digit dataset with a pre-trained PyTorch model.
🔢 Use cross-validation to generate out-of-sample predicted probabilities for every example in the dataset.
🏷️ Run one line of cleanlab code on these predicted probabilities to identify which audio clips may be mislabeled.
📰 Blog Post + Google Colab: https://cleanlab.ai/blog/label-errors-audio-datasets/
https://preview.redd.it/vpzhwg6jebw81.png?width=1260&format=png&auto=webp&s=3fa79d8a097ae5936e5e6a51e87508adf6190835
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Automated CV Pipelines 4th part is open for registration. It will be covering some of the best practices for video-specific annotation tasks.
If you are interested you can check out the details here!
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Today, AWS announced the general availability of Amazon Rekognition Streaming Video Events, a fully managed service for camera manufacturers and service providers that uses machine learning (ML) to detect objects such as people, pets, and packages in live video streams from connected cameras. Amazon Rekognition Streaming Video Events sends them a notification as soon as […]
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3xLOGIC is a leader in commercial electronic security systems. They provide commercial security systems and managed video monitoring for businesses, hospitals, schools, and government agencies. Managed video monitoring is a critical component of a comprehensive security strategy for 3xLOGIC’s customers. With more than 50,000 active cameras in the field, video monitoring teams face a daily […]
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Abode Systems (Abode) offers homeowners a comprehensive suite of do-it-yourself home security solutions that can be set up in minutes and enables homeowners to keep their family and property safe. Since the company’s launch in 2015, in-camera motion detection sensors have played an essential part in Abode’s solution, enabling customers to receive notifications and monitor […]
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Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes. With Data Wrangler, you can select and query data with just a few clicks, quickly transform data with over 300 built-in data transformations, and understand your data with built-in visualizations without writing any code. Additionally, […]
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Searchmetrics is a global provider of search data, software, and consulting solutions, helping customers turn search data into unique business insights. To date, Searchmetrics has helped more than 1,000 companies such as McKinsey & Company, Lowe’s, and AXA find an advantage in the hyper-competitive search landscape. In 2021, Searchmetrics turned to AWS to help with […]
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Identifying paraphrased text has business value in many use cases. For example, by identifying sentence paraphrases, a text summarization system could remove redundant information. Another application is to identify plagiarized documents. In this post, we fine-tune a Hugging Face transformer on Amazon SageMaker to identify paraphrased sentence pairs in a few steps. A truly robust […]
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This is a guest post by Moovit’s Software and Cloud Architect, Sharon Dahan. Moovit, an Intel company, is a leading Mobility as a Service (MaaS) solutions provider and creator of the top urban mobility app. Moovit serves over 1.3 billion riders in 3,500 cities around the world. We help people everywhere get to their destination […]
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Featuring stunning visuals from futuristic interstellar worlds, including colossal sand creatures, Dune captivated audiences around the world. The sci-fi film picked up six Oscars last month at the 94th Academy Awards, including for Best Sound and Visual Effects. Adapted from Frank Herbert’s 1965 novel of the same name, Dune tells the story of Paul Atreides, Read article >
The post How DNEG Helped Win Another Visual-Effects Oscar by Bringing ‘Dune’ to Life With NVIDIA RTX appeared first on NVIDIA Blog.
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It’s a jam-packed GFN Thursday. This week brings the popular, free-to-play, action role-playing game Lost Ark to gamers across nearly all their devices, streaming on GeForce NOW. And that’s not all. GFN Thursday also delivers an upgraded experience in the 2.0.40 update. M1-based MacBooks, iMacs and Mac Minis are now supported natively. Plus, membership gift Read article >
The post Your Odyssey Awaits: Stream ‘Lost Ark’ to Nearly Any Device This GFN Thursday appeared first on NVIDIA Blog.
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Workshop hosted by MIT’s Climate and Sustainability Consortium, MIT-IBM Watson AI Lab, and the MIT Schwarzman College of Computing highlights how new approaches to computing can save energy and help the planet.
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Competitive seed grants launch yearlong investigations of novel hypotheses about potential causes, biomarkers, treatments of Alzheimer’s and ALS.
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I've made surgeon https://github.com/archinetai/surgeon-pytorch, a small library to inspect the intermediate output layers of pyTorch models without changing the original implementation.
This can be very useful if you are using pre-trained models (e.g. from Huggingface or torch.hub) and want to get embeddings, attention matrices, or simply debug the model without adding additional code – which is often hard to do without changing the implementation.
I hope this can be helpful to anyone!
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I stumbled upon GFlow Net and in my opinion, it looks very similar to diffusion models. There is a touch of RL in GFlow Net but the main idea is very similar to diffusion models. is that right? or am I missing something?
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Hello everyone,
this post is about generative models! (i.e. Score-based-generative models, GANs, etc.)
on leaderboards like this https://paperswithcode.com/sota/image-generation-on-cifar-10
How do they check if the models do not just memorize the training examples? The FID score would be optimal in case you would just generate training examples again.
Best
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I was playing around with several AI models and this sort of stuff happens all the time.
https://preview.redd.it/6arq56nih3w81.png?width=1163&format=png&auto=webp&s=5ca68dce977ae671b0b928c2f1ec3cdd8478257f
I decided to prepare a compilation and rank the answers. Can you help me with deciding how bad or good are some of the answers by taking a survey here?
Edit: I apologize if some of you felt tricked into completing the survey by the original version of the post. It does have about 50 questions but they are mostly just yes/no, good/bad, so it shouldn't take longer than 10 minutes. I intend to write a piece about how AI assistants are doing and prepare a compilation of AI fails. I will share the results :)
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Linking techniques from machine learning with advanced numerical simulations, MIT researchers take an important step in state-of-the-art predictions for fusion plasmas.
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A hundred and forty turbines in the North Sea — and some GPUs in the cloud — pumped wind under the wings of David Standingford and Jamil Appa’s dream. As colleagues at a British aerospace firm, they shared a vision of starting a company to apply their expertise in high performance computing across many industries. Read article >
The post Answers Blowin’ in the Wind: HPC Code Gives Renewable Energy a Lift appeared first on NVIDIA Blog.
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Entrepreneur Jason Mars calls conversation our “first technology.” Before humans invented the wheel, crafted a spear or tamed fire, we mastered the superpower of talking to one another. That makes conversation an incredibly important tool. But if you’ve dealt with the automated chatbots deployed by the customer service arms of just about any big organization Read article >
The post What Is Conversational AI? ZeroShot Bot CEO Jason Mars Explains appeared first on NVIDIA Blog.
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TinyML is a groundbreaking technology! Possessing a lot of potential it is sure to grow exponentially in the coming years.
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Hey, we've been building Baseten to be able quickly deploy models, backends and frontends. I'd love to get your feedback.
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We've recently added Hugging Face support to https://github.com/graphsignal/graphsignal profiler, which I'd like to share in case someone finds it useful in their efforts to optimize speed and compute. More details, code and screenshots in the blog post https://graphsignal.com/blog/benchmarking-and-profiling-hugging-face-training-with-graphsignal/.
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Researchers from MIT/Meta recently released a new framework for unsupervised sentence embedding.
The performance seems to be better than SimCSE, the previous SOTA, by 2.3 absolute points on downstream tasks.
The pretrained models are available on Huggingface. GitHub: https://github.com/voidism/DiffCSE arXiv: https://arxiv.org/abs/2204.10298
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Hi. We recently did some work on using language models for molecule captioning and text-based molecule generation. You can think of it as doing translation between molecules and natural language.
Would love to know if you have any feedback 🤗. Arxiv: https://arxiv.org/abs/2204.11817
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https://blog.r2c.io/the-future-of-apps-intelligence/
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As featured on the Lex Fridman podcast, the Singularity weblog podcast and the Future of Life Institute podcast
The channel features shorts of Joscha's opinions and perspectives edited from podcasts.
You can check out the trailer, which mostly consists of podcast hosts' minds imploding.
All channel videos
Channel playlists
Channel creator: /u/24karate
Enjoy 🤖
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In this post, we demonstrate Kubeflow on AWS (an AWS-specific distribution of Kubeflow) and the value it adds over open-source Kubeflow through the integration of highly optimized, cloud-native, enterprise-ready AWS services. Kubeflow is the open-source machine learning (ML) platform dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable. Kubeflow provides many […]
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In this post, we walk you through two sampling techniques in Amazon SageMaker Data Wrangler so you can quickly create processing workflows for your data. We cover both random sampling and stratified sampling techniques to help you sample your data based on your specific requirements. Data Wrangler reduces the time it takes to aggregate and […]
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The adoption of AWS cloud technology at NatWest Group means moving our machine learning (ML) workloads to a more robust and scalable solution, while reducing our time-to-live to deliver the best products and services for our customers. In this cloud adoption journey, we selected the Customer Lifetime Value (CLV) model to migrate to AWS. The […]
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This is the third post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. This post is intended for data scientists, MLOps engineers, and data engineers who are interested in building ML pipeline templates with Amazon SageMaker. […]
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This is the second post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. In this post, we share how the NatWest Group utilized AWS to enable the self-service deployment of their standardized, secure, and compliant MLOps […]
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This is the first post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS to build a scalable, secure, and sustainable machine learning operations (MLOps) platform. This initial post provides an overview of the AWS and NatWest Group joint team implemented Amazon SageMaker Studio as the standard for […]
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Amazon SageMaker Data Wrangler is a new capability of Amazon SageMaker that helps data scientists and data engineers quickly and easily prepare data for machine learning (ML) applications using a visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Today, […]
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Data scientists face a problem: machine learning models need to be trained on labeled datasets, but labeling the data is tedious and time-consuming. Enter automatic data labeling, in which most of the preprocessing work is done by a computer. At first glance, automatic data labeling sounds too good to be true. Of course, more automation… Read More »2 Ways in Which Automatic Data Labeling Saves Time and Costs
The post 2 Ways in Which Automatic Data Labeling Saves Time and Costs appeared first on Data Science Central.
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As the Omicron variant of Covid-19 surged around the globe in 2021, managers who had begun contingency plans for a return to the office quietly shelved them to wait out the next wave. Heading into the summer of 2022, the omicron-delta variant lurks on the horizon, though whether or not this will trigger the massive… Read More »DSC Weekly Newsletter 26 April 2022: Why The Case for RTO Remains Weak
The post DSC Weekly Newsletter 26 April 2022: Why The Case for RTO Remains Weak appeared first on Data Science Central.
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Stakeholder Journey Maps are a fabulous tool to intimately understand what a stakeholder is trying to accomplish (their objectives and intentions) and the steps/actions/decisions that stakeholder needs to make to complete their journey. Stakeholder Journey Maps are commonly used to help designers to create the optimal user interface and nicely segue into UI storyboards and… Read More »Using Stakeholder Journey Maps to Re-invent, not Just Optimize, Your Business Processes
The post Using Stakeholder Journey Maps to Re-invent, not Just Optimize, Your Business Processes appeared first on Data Science Central.
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Data science is a wide field with many specializations, and an individual can have a great career with a data science degree. However, curriculums vary between schools, and the specific data science classes taught in one school may not be taught in another. There are several core skills in the data science field that recruiters… Read More »Make Sure Your Online Data Science Courses Teach These 6 Core Skills
The post Make Sure Your Online Data Science Courses Teach These 6 Core Skills appeared first on Data Science Central.
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Yes and no. So, if you asked this question, good one! When I was new to this stuff, I had the same question and searched up a lot about it.
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This week In the NVIDIA Studio, we’re launching the April NVIDIA Studio Driver with optimizations for the most popular 3D apps, including Unreal Engine 5, Cinema4D and Chaos Vantage. The driver also supports new NVIDIA Omniverse Connectors from Blender and Redshift.
The post In the NVIDIA Studio: April Driver Launches Alongside New NVIDIA Studio Laptops and Featured 3D Artist appeared first on NVIDIA Blog.
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A new artificial intelligence technique only proposes candidate molecules that can actually be produced in a lab.
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https://youtu.be/povBDxUn1VQ
Automatic curriculum generation is one of the most promising avenues for Reinforcement Learning today. Multiple approaches have been proposed, each with their own set of advantages and drawbacks. This paper presents ACCEL, which takes the next step into the direction of constructing curricula for multi-capable agents. ACCEL combines the adversarial adaptiveness of regret-based sampling methods with the capabilities of level-editing, usually found in Evolutionary Methods.
OUTLINE:
0:00 - Intro & Demonstration
3:50 - Paper overview
5:20 - The ACCEL algorithm
15:25 - Looking at the pseudocode
23:10 - Approximating regret
33:45 - Experimental results
40:00 - Discussion & Comments
Website: https://accelagent.github.io
Paper: https://arxiv.org/abs/2203.01302
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Hello, I wrote the blog post about text-conditional image generation using diffusion models (including DALLE-2). Let me know what you think!
https://sangyun884.github.io/recent-trends-in-diffusion-based-text-conditional/
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Hey,
🤗 Hugging Face is offering a workshop (June 6) for instructors of machine learning and data science who would like to learn how the open-source ecosystem can be used in their classes.
After this workshop, you will know how to:
🧑💻 Teach Transformers models & famous ML libraries
🤖 Onboard students to the Hub to build/host projects
💾 Publish models/datasets in a few lines of code
During the workshop, you will be invited to join the following page for a better understanding of our open-source solutions: https://huggingface.co/teach
For more details about the workshop content, visit: https://hf.co/teaching
Feel free to register here:)
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Hi, I am having some trouble with a LSTM problem regarding walk forward validation in my LSTM. The problem is described in this stackoverflow post:
https://stackoverflow.com/questions/71990833/using-predictions-instead-of-observed-values-in-walk-forward-validation-in-lstm
If any one could help me that would be much appreciated
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The last few years have seen rapid growth in the field of natural language processing (NLP) using transformer deep learning architectures. With its Transformers open-source library and machine learning (ML) platform, Hugging Face makes transfer learning and the latest transformer models accessible to the global AI community. This can reduce the time needed for data […]
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Nordic Aviation Capital (NAC) is the industry’s leading regional aircraft lessor, serving almost 70 airlines in approximately 45 countries worldwide. In 2021, NAC turned to AWS to help it use artificial intelligence (AI) to further improve its leasing operations and reduce its reliance on manual labor. With Amazon Rekognition Custom Labels, NAC built an AI […]
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Researchers have developed a technique that enables a robot to learn a new pick-and-place task with only a handful of human demonstrations.
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Picture a person walking in a park by a pond. The surrounding environment contains a number of moving objects that change the quality of the environment: clouds moving to hide the sun, altering the quality of light; ducks gliding across the pond, causing its surface to ripple; people walking along a path, their images reflecting […]
The post PPE: A fast and provably efficient RL algorithm for exogenous noise appeared first on Microsoft Research.
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When brainstorming a scene to best showcase the groundbreaking capabilities of the Omniverse platform, some NVIDIA artists turned to a cherished memory: enjoying ramen together in a mom-and-pop shop down a side street in Tokyo. Simmering pots of noodles, steaming dumplings, buzzing kitchen appliances, warm ambient lighting and glistening black ledger stools. These were all Read article >
The post Let Me Shoyu How It’s Done: Creating the NVIDIA Omniverse Ramen Shop appeared first on NVIDIA Blog.
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A paper released today describes in the greatest detail to date the atmospheres on distant planets. Seeking the origins of what’s in and beyond the Milky Way, researchers surveyed 25 exoplanets, bodies that orbit stars far beyond our solar system. Specifically, they studied hot Jupiters, the largest and thus easiest to detect exoplanets, many sweltering Read article >
The post Stellar Weather: Researchers Describe the Skies of Exoplanets appeared first on NVIDIA Blog.
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At some point, we’ve all used Google translate, Microsoft,DeepL or Bing translator to impress our friends/colleagues who speak a different…
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https://preview.redd.it/beo6czc6hhv81.png?width=1706&format=png&auto=webp&s=385f65ebfed9344781d1f9238d8d508dae27c0a3
In the last decade, AI research has brought astonishing results in many fields, and, undoubtedly, AI is nowadays a central technology in many aspects of our life. As new ideas are proposed every day, this continuous research usually comes with infinite applications: from the algorithms assisting surgeons in complex operations to the one which allows unlocking our phone using just our face. In this evolution from the idea to the actual implementation, it is often ignored how hard the passage between theoretical research and working application is.
We can refer to this process as AI Development Cycle for Edge AI and can be divided into three phases related to 1) data, 2) model, and 3) evaluation.
Many aspects must be considered: first, each different AI application requires a specific dataset. For this reason, in this step, the aim is to prepare the data, which, as is well known, is one of the crucial topics of AI: a good algorithm always relies on a good dataset. This phase can be divided into data collection, curation, labeling, and preparation.
Continue reading
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In the last decade, AI research has brought astonishing results in many fields, and, undoubtedly, AI is nowadays a central technology in many aspects of our life. As new ideas are proposed every day, this continuous research usually comes with infinite applications: from the algorithms assisting surgeons in complex operations to the one which allows unlocking our phone using just our face. In this evolution from the idea to the actual implementation, it is often ignored how hard the passage between theoretical research and working application is.
We can refer to this process as AI Development Cycle for Edge AI and can be divided into three phases related to 1) data, 2) model, and 3) evaluation.
Many aspects must be considered: first, each different AI application requires a specific dataset. For this reason, in this step, the aim is to prepare the data, which, as is well known, is one of the crucial topics of AI: a good algorithm always relies on a good dataset. This phase can be divided into data collection, curation, labeling, and preparation.
Continue reading
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I selected 5 ready-made algorithms for face detection and compared them with each other by such metrics as Precision, Recall, IOU and time on the dataset I marked up. I am ready to accept your Pull Request with your solutions(algorithms) and results!
GitHub: https://github.com/wb-08/face-detection-algorithms-comparison
Blog post: https://habr.com/ru/post/661671/
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https://www.youtube.com/watch?v=2E_6ARbrMmc
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Github: https://github.com/labmlai/neox
Annotated implementation: https://lit.labml.ai/github/labmlai/neox/tree/main/src/neox/__init__.py
Original repo from EleutherAI: https://github.com/EleutherAI/gpt-neox
We have included samples showing how to generate text and to fine-tune. We haven't included a bunch of optimizations that were present in original GPT-NeoX to keep things simple.
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Hello!
If you are using jax and you need to pass some pytrees between processes, I may have something for you :)
I developed a "treequeue". It is a queue that is made for pytree's nested arrays.
The transfer speed is up to 10 times higher than regular queues. This is done by utilizing shared memory arrays and avoiding pickling data. This can be very useful when developing distributed architecture, e.g. distributed reinforcement learning where speed is at the upmost importance.
In my case this implementation was very useful to remove bottlenecks when implementing PBT algorithms!
https://github.com/thomashirtz/treequeues
Cheers!
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Comes with ‘white paper’ and example notebooks… seems legit..? Anyone tried this out yet?
Github
Paper]
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I know that many people (including me) were surprised after seeing the image quality of ViT-VQGAN and disappointed to know there won't be no source code released. Therefore, I've decided to implement it by myself and here is the code. I hope this can help everyone as a starting point for ViT-VQGAN.
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Programming is way more fun when you learn/work with someone. Help each other, ask questions, brainstorm, etc. There is just so much benefit to joining a community when you are in this field, especially when you cannot find the question you are looking for on stack overflow! 😉
This is the same thing with AI, and it is why a little less than two years ago I created a discord server. Where anyone learning or working in the field could come and share their projects, learn together, work together, and much more. The community has now over 20 000 members, which is unbelievable! So glad to see it growing and see everyone so active. We also have an amazing partnership with an AI company coming that is super exciting for the community. You definitely want to be there to enjoy all the benefits they will give us.
Come join us if you are in the field of AI !
https://discord.gg/learnaitogether
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https://arxiv.org/abs/2204.09123
https://www.researchgate.net/publication/360079336_GAMe_changer_or_not_An_evaluation_of_interpretable_machine_learning_models_based_on_additive_model_constraints
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Hi, I want to check policy-encoding mapping
e : (S → A) → R^k in Universal Successor Features Approximators.
I don't know how to embedding network to another network. There are too many weights! Do you have any ideas? Thank you for reading!
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Found a useful list of Tools, Frameworks, and Resources for RL/ML. It covers Reinforcement learning, Machine Learning (TensorFlow & PyTorch), Core ML, Deep Learning, Computer Vision (CV). I thought I'd share it for anyone that's interested
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AWS CodeArtifact allows developers to connect internal code repositories to upstream code repositories like Pypi, Maven, or NPM. AWS CodeArtifact is a powerful addition to CI/CD workflows on AWS, but it is similarly effective for code-bases hosted on a Jupyter notebook. This is a common development paradigm for Machine Learning developers that build and train […]
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Artificial Intelligence is all over the world today. From the use of virtual assistants like Siri, Alexa, or Cortana, to improving…
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Different parts of the globe are experiencing distinct climate challenges — severe drought, dangerous flooding, reduced biodiversity or dense air pollution. The challenges are so great that no country can solve them on their own. But innovative startups worldwide are lighting the way, demonstrating how these daunting challenges can be better understood and addressed with Read article >
The post By Land, Sea and Space: How 5 Startups Are Using AI to Help Save the Planet appeared first on NVIDIA Blog.
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Here's a tutorial and lightweight PyTorch implementation of VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. Hope you find it helpful!
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https://www.sganalytics.com/blog/top-ethical-challenges-in-ai-the-price-of-progress/
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https://github.com/marlbenchmark/on-policy/blob/main/onpolicy/algorithms/r_mappo/r_mappo.py
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Hi, I want to train my agent in the environment used in "Emergence of Locomotion Behaviours in Rich Environments". Here is a video about that https://www.youtube.com/watch?v=hx_bgoTF7bs. Is the environment released? Thanks for reading.
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Here's a tutorial and lightweight PyTorch implementation of VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. Hope you find it helpful!
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Dear ML researchers,
For the past many years, I've been updating my machine learning research notes for my PhD students and everyone online continuously. I don't like uploading to arxiv to get "citations", and GitHub serves me well: Hope they are useful for you:
https://github.com/roboticcam/machine-learning-notes
Richard,
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https://www.amazon.science/blog/amazon-releases-51-language-dataset-for-language-understanding
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The Norwegian University of Science and Technology (NTNU) has a vacancy for PhD Candidate within the DIGITALSEAICE project . The project aims to build a multi-scale digital infrastructure that integrates local and regional sea ice models for improved forecasting and understanding of variations in polar ice conditions. More information here: https://www.jobbnorge.no/en/available-jobs/job/224802/
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Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. Amazon Textract now offers the flexibility to specify the data you need to extract from documents using the new Queries feature within the Analyze Document API. You don’t need to know the structure of the […]
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Yes! You read the heading right. There’s indeed a difference between loss functions and Metrics in the field of Machine Learning. However…
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A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, cyclists, and pedestrians in real-time.
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Your next trip to the dentist might offer a taste of AI. Pearl, a West Hollywood startup, provides AI for dental images to assist in diagnosis. It landed FDA clearance last month, the first to get such a go-ahead for dentistry AI. The approval paves the way for its use in clinics across the United Read article >
The post Tooth Tech: AI Takes Bite Out of Dental Slide Misses by Assisting Doctors appeared first on NVIDIA Blog.
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The gods must be smiling this GFN Thursday — God of War today joins the GeForce NOW library. Sony Interactive Entertainment and Santa Monica Studios’ masterpiece is available to stream from GeForce NOW servers, across nearly all devices and at up to 1440p and 120 frames per second for RTX 3080 members. Get ready to Read article >
The post GFN Thursday Is Fit for the Gods: ‘God of War’ Arrives on GeForce NOW appeared first on NVIDIA Blog.
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If you are employed as a data scientist and have survived (or strived!) in your position for more than a year, chances are you are at least a good data scientist. This is particularly true if you were promoted. The difference between a mediocre and a good data scientist will be the topic of a… Read More »18 Differences Between Good and Great Data Scientists
The post 18 Differences Between Good and Great Data Scientists appeared first on Data Science Central.
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Diffusion models have been behind a recent string of impressive generative results, including OpenAI's DALL-E 2. They’re powered by a simple yet expressive core mechanism. New video covering how they work: https://youtu.be/fbLgFrlTnGU
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https://github.com/sovit-123/yolov1_pytorch_voc07
Also, I write about Deep Learning and Machine Learning on https://debuggercafe.com/
Please check it out and let me know if somebody wants any blog posts on a specific topic.
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A few years back I developed a very simple app just to show a few bible verses. Though it is a very simple app, it got more than 50K installs without much promotion. So, I am thinking about promoting it. But hesitate to do it as it is very simple app. So, I would like to add some useful feature before start promoting it. I would like to add a feature which will allow the users to ask any question related to bible, and it should be giving relevant answer. I assume that some bible data is open source.
Is there any free tutorial available to know about how to implement AI based chat system for answering any bible related queries after training with bible data.
Is there any app already providing this feature?
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But can technology be controlled to avoid adverse outcomes?
Let's understand how AI will help us to make a better world.
https://us.sganalytics.com/blog/top-ethical-challenges-in-ai-the-price-of-progress/
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To start deeply investigating the AI app development process, it’s important to first understand how these projects differ from regular app…
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I just moved. I’d like to say that I was highly organized, that I knew where every box ended up and what was in each box. I would be lying. Most people who move know the feeling of living in boxes even after the movers have left, the days spent dodging labyrinths of teetering cardboard,… Read More »DSC Weekly Digest 4/19/2022: The Case for Personal Knowledge Graphs
The post DSC Weekly Digest 4/19/2022: The Case for Personal Knowledge Graphs appeared first on Data Science Central.
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Organizations use collaborative document authoring solutions like Salesforce Quip to embed real-time, collaborative documents inside Salesforce records. Quip is Salesforce’s productivity platform that transforms the way enterprises work together, delivering modern collaboration securely and simply across any device. A Quip repository captures invaluable organizational knowledge in the form of collaborative documents and workflows. However, finding […]
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Your regulatory data project likely has no use case for design-time data lineage. tl/dr Mapping Data Lineage at design time, for its own end, has no regulatory use case or ROI. Buying a specialist tool to support that mapping has even less ROI. Regulations see that kind of documentary data lineage as ancillary at best.… Read More »Do regulatory data projects really need design-time data lineage? Probably not.
The post Do regulatory data projects really need design-time data lineage? Probably not. appeared first on Data Science Central.
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Hi there! Chatbots are not the "set and forget" thing like many other software. If you want to achieve great results with your chatbot, you need to improve it constantly. To know where and what to improve, you need to track and monitor chatbot analytics and the main chatbot metrics.
General chatbot metrics
Total number of users
User satisfaction
Accuracy of the chatbot
Engagement metrics
Active users
New users
Conversation Length
Retention Rate
Bounce Rate
Flow Completion Rate
Conversational analytics
Goal Completion Rate (GCR)
Fallback Rate
Human Takeover Rate
* Bonus: Revenue metrics
Revenue generated
ROI / payback period
Here in the article we covered how to calculate each metrics, and you can find needed metrics depending on the industry you working in https://botscrew.com/blog/chatbot-metrics/?utm_source=RedditArtificial&utm_medium=&utm_campaign=&utm_term=&utm_content=
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This
I looked through the available ones, not a single one seems to match it. Sorry if this isn't the right sub to ask, but since Replica Studios doesn't have its own sub I don't know where
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I've been writing tutorials with Pinferencia and HuggingFace.
HuggingFace is quite handy and easy to use.
I want to write some tutorial about computer vision afterwards.
Is there anything similar in Computer vision area?
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You can now load multiple transformers (each model has a unique sparsification recipe) on top of the DeepSparse server behind Streamlit, and it's open-source. This was battle tested on a 16GB of RAM with only 4 core CPU virtual machine. These compute requirements are enough to load up to 19 sparse BERT models in memory and compare their performance on question answering (P.S. they are really fast on just CPUs).
💻code: https://github.com/neuralmagic/deepsparse/tree/main/examples/sparseserver-ui
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This is a light implementation of the idea in the paper Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices. Instead of finding uncertainties I have added Jain's Fairness Index as a addition to the loss function.
Gist: https://gist.github.com/Gananath/8d167384da7d3bc078650c73fab1a8dd
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Sponsored Post Unlike traditional online courses, Foster Provost’s workshops will give you the chance to engage live with a world-class […]
The post 10 seats remaining | A series of live ML strategy workshops appeared first on Machine Learning Mastery.
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Conversational interfaces (or chatbots) can provide an intuitive interface for processes such as creating and monitoring tickets. Let’s consider a situation in which a recent hire on your team is required to cut tickets for office equipment. To do so, they have to interact with a ticketing software that the organization uses. This often requires […]
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Edge computing has come of age, with deployments enabling many applications that process data from IoT sensors and cameras. In 2017, we identified the symbiotic relationship between edge computing and video analytics in an article, noting that live video analytics is the “killer app” for edge computing. Edge devices come in various shapes and sizes […]
The post Don’t let data drift derail edge compute machine learning models appeared first on Microsoft Research.
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Shared duties have always been the most critical component of every successful organization, regardless of its nature or size. When it…
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Creating content is no longer tethered to using paint and stone as mediums, nor being in massive studios. Visual art can now be created anywhere, anytime. But being creative is still challenging and time-consuming. NVIDIA is making artistic workflows easier and faster by giving creators tools that enable them to remain in their flow state. Read article >
The post Welcome ‘In the NVIDIA Studio’: A Weekly Celebration of Extraordinary Artists, Their Inspiring Art and Innovative Techniques appeared first on NVIDIA Blog.
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https://lastweekin.ai/p/163?s=w
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Hi all! My team recently reproduced and published a PyTorch implementation of the paper SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition.
Our blog post walks through the code and provides a detailed explanation of the architecture they use in order to perform object segmentation on videos in a fully self-supervised manner.
Hope this is helpful/interesting to others!
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FaceNext is an open source PyTorch library for high fidelity 3D face reconstruction from single/multiple RGB image(s).
github.com/abdallahdib/NextFace
https://reddit.com/link/u6e7cd/video/ixg0wlzirau81/player
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Hi, I have just published my latest medium article.
Anomalies are widespread when it comes to working on data. They become vital in time series. So, It is crucial to propose efficient methods to detect and deal with them. This article illustrates a state-of-the-art model called DGHL for anomaly detection. DGHL includes a ConvNet as a Generator and instead of encoding it maximizes the likelihood with the Alternating Back-Propagation algorithms.
https://rezayazdanfar.medium.com/deep-generative-model-with-hierarchical-latent-factors-for-time-series-anomaly-detection-8d6eaebad8bc
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7 use-cases where you can make your python code more nifty, concise and elegant — without compromising readability.
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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In my first blog of the series “Fallacy of Becoming Data-driven – Part 1: Becoming Value-obsessed”, I preached about the critical importance of reframing the conversion away from data-driven to becoming value-obsessed. Instead of focusing on becoming value-driven, organizations need to focus on how to uncover the customer, product, service, and operational insights buried in… Read More »Fallacy of Becoming Data-driven – Part 2: Cultural Transformation
The post Fallacy of Becoming Data-driven – Part 2: Cultural Transformation appeared first on Data Science Central.
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Data has become a huge area of business, helping businesses to drive their intelligence, make better decisions, and formulate strategic plans for future growth.
The post Using Data Warehousing as a Service (DWaaS) To Improve Customer Experience appeared first on Data Science Central.
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https://youtu.be/XHAoV_nKr1o
This video explains the 10 billion parameter SEER model from MetaAI by Goyal et al. 2022.
Paper link: https://arxiv.org/abs/2202.08360
Official implementation: https://github.com/facebookresearch/vissl/tree/main/projects/SEER
Short description:
The 10 billion parameter SEER model from u/MetaAI is *fairer*, even though it is trained on *uncurated* data. How so? Check out our take on this.
Outline:
00:00 Training on uncurated data
01:12 Diffgram (Sponsor)
01:46 Toxicity in large models
02:43 What to do against model toxicity?
03:53 SEER model explained
06:52 SEER is fairer. But how?
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LinkedIn research team has recently open-sourced feature store, Feathr, created to simplify machine learning (ML) feature management and increase developer productivity. Feathr is used by dozens of LinkedIn applications to define features, compute them for training, deploy them in production, and share them across consumers. Compared to previous application-specific feature pipeline solutions, Feathr users reported significantly reduced time required to add new features to model training and improved runtime performance.
Hundreds of ML models run on LinkedIn in Search, Feed, and Ads applications. Thousands of features about entities in the Economic Graph, such as companies, job postings, and LinkedIn members, power the models. The most time-consuming aspects of handling the ML applications at scale have been preparing and managing features.
Continue reading the summary
Github: https://github.com/linkedin/feathr
LinkedIn Blog: https://engineering.linkedin.com/blog/2022/open-sourcing-feathr—linkedin-s-feature-store-for-productive-m
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Multimodal: A python package to ensemble speech, text, etc. models and build new applications. Sample Applications: Speech Named Entity Anonymizer, Speech Question Answering, Speech Generation
Code: kritiksoman/Multimodal: Listen. Write. Speak. Read. Think. (github.com)
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In a new announcement, Bounding.ai launched its marketplace for computer vision and AI teams to access training data easily. The platform is designed to empower individuals and small companies around the world to create and sell datasets that will be instantly accessible by any team in need of labeled data.
Bounding.ai Launches New Marketplace for AI Labeled Data & $5,000 Prize
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Hi, I'm the creator of Pinferencia. Currently I'm design new features to-do list. I want to know:
Do you train and deploy models using just one framework or multiple frameworks at work?
For example, use pytorch for training and deployment, or use tensorflow, pytorch for training, onnx for deployment.
View Poll
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Randomly sampled MNIST output. It's not good I know.
Hi, I noticed there aren't that many simple implementation of DDPM, for example, using MNIST. I had to make a presentation for my workplace seminar, so I had to implement the simplified version of DDPM myself. The whole thing is under 200 lines of code
https://github.com/cloneofsimo/minDiffusion
This implementation has MANY missing details, such as Unet Models etc. I think it is worth taking a look, especially if you are interested in recent boom of diffusion models (such as Dalle 2)
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Automatic speech recognition (ASR) is a commonly used machine learning (ML) technology in our daily lives and business scenarios. Applications such as voice-controlled assistants like Alexa and Siri, and voice-to-text applications like automatic subtitling for videos and transcribing meetings, are all powered by this technology. These applications take audio clips as input and convert speech […]
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Banking and financial institutions review thousands of credit applications per week. The credit approval process requires financial organizations to invest time and resources in reviewing documents like W2s, bank statements, and utility bills. The overall experience can be costly for the organization. At the same time, organizations have to consider borrowers, who are waiting for […]
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Hey all,
There's a new course on the Neuroevolution of Augmenting Topologies (NEAT) algorithm. It's a niche algorithm, but uses some very interesting mechanisms to train/evolve simple irregular neural networks.
Thought some of you may be interested.
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Gil Makleff and Artem Koren are developing AI for meeting transcripts, creating time-savers like shareable highlights of the text that is often TL;DR (too long; didn’t read). The Sembly founders conceived the idea after years of working in enterprise operational consulting at UMT Consulting Group, which was acquired by Ernst & Young. “We had an Read article >
The post Startup Transforms Meeting Notes With Time-Saving Features appeared first on NVIDIA Blog.
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Talk about a bright idea. A team of scientists has used GPU-accelerated deep learning to show how color can be brought to night-vision systems. In a paper published this week in the journal PLOS One, a team of researchers at the University of California, Irvine led by Professor Pierre Baldi and Dr. Andrew Browne, describes how Read article >
The post A Night to Behold: Researchers Use Deep Learning to Bring Color to Night Vision appeared first on NVIDIA Blog.
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Here’s the truth.
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A multidisciplinary team of graduate students helps infuse ethical computing content into MIT’s largest machine learning course.
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Automated CV Pipelines 3rd part is open for registration. It will be covering the methods of streamlining instance classification. If you are interested to check out, here is the link to register.
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Thirty years it's taken me, A.I. that is not just as good as humans but better than humans at composing music:
https://i.imgur.com/hReXJq1.png
It passes the Turing Test, and it is also a revolution in the field of music in and of itself.
In the meantime, no one has said anything nice to me in thirty years; just insults. I would feel dumb rewarding humanity with my creation; it would send the wrong message; it would affirm their bad behavior. Garbage species. Low IQ.
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Fellowship funds graduate studies for outstanding immigrants and children of immigrants.
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AI Weirdness: the strange side of machine learning
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An inside look at how REINFORCEMENT learning, without past reference, extracts “optimal” decisions through simple interaction …
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This GFN Thursday delivers more gr-EA-t games as two new titles from Electronic Arts join the GeForce NOW library. Gamers can now enjoy Need for Speed HEAT and Plants vs. Zombies Garden Warfare 2 streaming from GeForce NOW to underpowered PCs, Macs, Chromebooks, SHIELD TV and mobile devices. It’s all part of the eight total Read article >
The post GFN Thursday Gears Up With More Electronic Arts Games on GeForce NOW appeared first on NVIDIA Blog.
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Globally, many think that data scientist is the best job after Harvard declared it to be one of the hottest jobs of the decade. And since then, many have been choosing it as their career path. But the role of a data engineer is as important as the data scientist is, because if a data… Read More »Why Data Engineers are in Greater Demand than Data Scientists
The post Why Data Engineers are in Greater Demand than Data Scientists appeared first on Data Science Central.
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Hi, I’m a machine learning platform engineer. I’ve been using, exploring and developing model deployment tools and platform for several years.
Very often, I found that many of the tools or managed service of AI platform, are not very welcome by many users. Some think these tools are unnecessarily complicated.
I'm currently developing a library in my free time trying to fill the gap. And I also want the library to get well integrated with most users' deployment environments.
Would you like to share how and where do you serve your model? Using kubernetes? Self developed or existing tools? Thanks~
P.S. If you are interested, you can visit my project to submit an issue/PR or join the discussions, welcome to help: Pinferencia
View Poll
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I’m sure we all remember the story of “The Little Engine That Could.” A little railroad engine was built for pulling a few cars on and off the switches. When more powerful engines are asked to pull a load over a steep hill, they respond “I can’t; that is too much a pull for me”.… Read More »Fallacy of Becoming Data-driven – Part 1: Becoming Value-obsessed
The post Fallacy of Becoming Data-driven – Part 1: Becoming Value-obsessed appeared first on Data Science Central.
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You can establish feature stores to provide a central repository for machine learning (ML) features that can be shared with data science teams across your organization for training, batch scoring, and real-time inference. Data science teams can reuse features stored in the central repository, avoiding the need to reengineer feature pipelines for different projects and […]
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Amazon Lex can add powerful automation to contact center solutions, so you can enable self-service via interactive voice response (IVR) interactions or route calls to the appropriate agent based on caller input. These capabilities can increase customer satisfaction by streamlining the user experience, and improve containment rates in the contact center. In both the self-service […]
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What does 2022 look like for AI? Let's find out.
https://us.sganalytics.com/blog/top-ethical-challenges-in-ai-the-price-of-progress/
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New program strives to bridge the talent gap for underrepresented groups in the tech industry.
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Perovskite materials would be superior to silicon in PV cells, but manufacturing such cells at scale is a huge hurdle. Machine learning can help.
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Hi all, I am creating a multiple-goal environment. Which is an 8*8 discrete map with a start and terminal state (only one) change after each episode. The reward is 100 for reaching the terminal state and -1 for the rest. In fact, I am not sure if the reward is reasonable.
I used PPO from SB3 and I can easily finish it. But when I go offline, using HER+DQN, the training is very bad.
Feel free to run it here or take a look at the env and training result. Thank you so much!
https://colab.research.google.com/drive/1Mt5Yje7GTyjOBHL09zC9C1L05xpTAK9v?usp=sharing
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In deep learning and machine learning, having a large enough dataset is key to training a system and getting it to produce results. So what does a ML researcher do when there just isn’t enough publicly accessible data? Enter the MLCommons Association, a global engineering consortium with the aim of making ML better for everyone. Read article >
The post MLCommons’ David Kanter, NVIDIA’s Daniel Galvez on Improving AI with Publicly Accessible Datasets appeared first on NVIDIA Blog.
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Episode 135 | April 13, 2022 In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary L. Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations. Dr. Gray and her team are working to bring computer science, engineering, social science, and […]
The post Just Tech: Centering Community-Driven Innovation at the Margins Episode 3 with Dr. Sasha Costanza-Chock appeared first on Microsoft Research.
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Roughly five years ago, I created this thread where I outlined my realization about the imminency of synthetic media.
This was before transformers blew up, before StyleGAN, before GPT-2, when WaveNet and DeepDream were still among the best we could do, and when predictive text algorithms that were barely better than Markov Chains were still the state of the art. In five short years, the state of artificial intelligence has changed overwhelmingly, to the point it's barely recognizable. Looking back to 2017, I now get this sense of everything feeling so primitive and fake. I've stated many times that AI before roughly 2019 was a bunch of digital magic tricks, and the field as a whole was essentially a giant Potemkin village that utilized clever sleight of hand and advertising to make it se…
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The copent package implements the method for estimating copula entropy (mutual information) and transfer entropy (conditional mutual information / conditional independence).
This version add a new feature (an argument 'mode') for dealing with large data when memory is limited.
Github: https://github.com/majianthu/pycopent
PyPI: https://pypi.org/project/copent/
any comments are welcome.
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Hi there, in the case if any of you use the openCV BFMatcher with NORM_L2, you can try to use my recent pet project: https://github.com/kmkolasinski/fast-bfmatcher
Basically the speed-up is achieved by using faster replacement for BLAS, a BLIS library and some custom implementations written in C and cython.
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With the advent of deep learning models, automated data extraction is becoming more accessible. In this article, we demonstrate step-by-step how to fine-tune layoutLM V2 on invoices starting from data annotation to model training and inference.
Enjoy the read and if you have any questions, leave them below.
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By Vincent Granville, Ph.D., Author at MLtechniques.com Sponsored Post Very deep neural networks (VDNN) illustrated with data animation: a 40 second […]
The post Very Deep Neural Networks Explained in 40 Seconds appeared first on Machine Learning Mastery.
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(A Sci-Fi Ultrashort)
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The programs are designed to foster an understanding of how artificial intelligence technologies work, including their social implications.
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Recently, we have released the source code of our winning solution for the NetHack 2021 NeurIPS Challenge:
https://github.com/maciej-sypetkowski/autoascend
We hope that it will help in leveraging this complex environment, that still seems to be beyond capabilities of reinforcement learning. Check out links in the README "Description" section for more context.
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I want to hear about your impressive environment! Specifically, I want to make my custom environment well using various library like openAI gym.
In this contxet, I find out the highway-env https://github.com/eleurent/highway-env/ !
I think this environment has convinient API for users.
Thus, I make my custom env with referencing the highway-env
https://preview.redd.it/ndl1h1dvpzs81.png?width=711&format=png&auto=webp&s=267320a9713d98e7e49c4bb89423e5a9612bad8e
In this line, could you speak your best environment? It doesn't matter about your best env has any advantage!
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I'm looking for a generative-LM equivalent of an EfficientNet-Lite, for inference on devices with limited to no VRAM. I know about some popular ones like DistilGPT2. But it's been 2 years after its release. Surely, someone improved their size/performance ratio, right... right?
Thank you for your time. 🤗
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Recurrent neural networks for brain time series - Sano Centre for Computational Personalised Medicine
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Profession in finance and accounting is one of the top career choices for finance and accounting professionals. Employment of accountants and auditors is projected to grow 7 percent from the year 2020 to the year 2030, about as fast as the average for all occupations. About 135,000 openings for accountants and auditors are projected each year,… Read More »Advance in your finance and accounting careers with top technical skills
The post Advance in your finance and accounting careers with top technical skills appeared first on Data Science Central.
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Hi guys. I am currently implementing REDQ by modifying a working implementation of SAC (basically adapted from Spinup) and so far my implementation doesn't work, I am trying to understand why. By looking at the authors' implementation I notice they use 1 pytorch optimizer per Q network, whereas I only use 1 for all parameters. So I wonder, is there any good reason for using several optimizers here?
Thanks!
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I released a repository of models with optional pretrained weights(Weights are taken from TF/Keras) to be used for tasks like prediction, feature extraction and fine-tuning.
Github: https://github.com/abarcel/haikumodels
Currently Available Models
MobileNet
ResNet [50, 101, 152]
VGG [16, 19]
Xception
Also planning to release more, as soon as I find time for it.
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Paper: https://arxiv.org/abs/2204.00598
https://socraticmodels.github.io/
Twitter: https://twitter.com/andyzengtweets/status/1512089759497269251
Abstract: " Large foundation models can exhibit unique capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are further trained on Internet-scale text with no images (e.g. from spreadsheets, to SAT questions). As a result, these models store different forms of commonsense knowledge across different domains. In this work, we show that this model diversity is symbiotic, and can be leveraged to build AI systems with structured Socratic dialogue -- in whi…
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New version of paper is linked to in the DALL-E 2 blog post and also here (pdf file format).
Tweet announcing updated paper.
Older version of paper (pdf file format).
Original Reddit post.
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A team of scientists have created a new AI-based tool to help lock up greenhouse gases like CO2 in porous rock formations faster and more precisely than ever before. Carbon capture technology, also referred to as carbon sequestration, is a climate change mitigation method that redirects CO2 emitted from power plants back underground. While doing Read article >
The post Rock On: Scientists Use AI to Improve Sequestering Carbon Underground appeared first on NVIDIA Blog.
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In many industries, it’s critical to extract custom entities from documents in a timely manner. This can be challenging. Insurance claims, for example, often contain dozens of important attributes (such as dates, names, locations, and reports) sprinkled across lengthy and dense documents. Manually scanning and extracting such information can be error-prone and time-consuming. Rule-based software […]
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Here's the blogpost estimating the cost.
What would it cost you to train PaLM using cloud computing (and you're not Google)? Something around $9M to $17M.
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I am thinking of building a graph classifier that takes in graphs and labels the incoming graph.
The dataset of interest to me is RadGraph: https://arxiv.org/abs/2106.14463
The issue I am having is that the graphs in RadGraph are disconnected in nature (on average 20 disconnected components), making it difficult for the various graph encoders I am aware of to do a good job classifying the graphs.
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Implementation here
Looks like they manually calculate the gradient? I'm very curious how much of a difference this makes!
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https://outsystems-ai-reading-group.github.io/
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Hi Everyone!
We have released the preprint and google colab demo for our paper FaceSigns. FaceSigns embeds a secret bit-string as a semi-fragile watermark in the image pixels. The message is recoverable if benign image operations such as color/contrast adjustment, JPEG compression, Instagram filters are applied. However, the message cannot be decoded if the image is facially tampered (eg. DeepFake manipulation) . This selective fragility allows reliable detection of DeepFake manipulations applied on images signed using FaceSigns.
Try out our google colab demo to see message encoding and decoding using FaceSigns!
Paper: https://arxiv.org/abs/2204.01960
Project Webpage: https://shehzeen.github.io/facesigns
Demo: https://github.com/paarthneekhara/FaceSignsDemo
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GeForce NOW is about bringing new experiences to gamers. This GFN Thursday introduces game demos to GeForce NOW. Members can now try out some of the hit games streaming on the service before purchasing the full PC version — including some finalists from the 2021 Epic MegaJam. Plus, look for six games ready to stream Read article >
The post Try This Out: GFN Thursday Delivers Instant-Play Game Demos on GeForce NOW appeared first on NVIDIA Blog.
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Out of curiosity, how long would it take to implement a paper like this one? https://arxiv.org/abs/2104.07750
It has PPO agents in MARL, all of them with multihead attention performed on the observation, in such a way that an attention map is created for each agent. This attention map has information about how strongly each agent is attending to various elements of the environment. With KL divergence, the agents are rewarded for minimizing the difference between their attention maps.
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Hey there, just a heads up we at The Gradient just published a new article discussing explainability -
"This article uses the common backdrop of competitive games to explore the ways in which domain experts adapt to new technologies that lack explainability. I illustrate how interpretations vary based on user experience and model architecture, and how special care must be taken when adapting models to human-centric problems."
Check it out here if you think it's interesting / worth discussing:
Reading the Tea Leaves: Expert End-Users Explaining the Unexplainable
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Meet the electric vehicle that’s quick-witted and fully outfitted. Last week, NIO began deliveries of its highly anticipated ET7 fully electric vehicle, in Hefei, China. The full-size luxury sedan is the first production vehicle built on the NIO Adam supercomputer, powered by four NVIDIA DRIVE Orin systems-on-a-chip (SoCs). The production launch of its flagship sedan Read article >
The post Fast and Luxurious: The Intelligent NIO ET7 EV Built on NVIDIA DRIVE Orin Arrives appeared first on NVIDIA Blog.
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In its debut in the industry MLPerf benchmarks, NVIDIA Orin, a low-power system-on-chip based on the NVIDIA Ampere architecture, set new records in AI inference, raising the bar in per-accelerator performance at the edge. Overall, NVIDIA with its partners continued to show the highest performance and broadest ecosystem for running all machine-learning workloads and scenarios Read article >
The post NVIDIA Orin Leaps Ahead in Edge AI, Boosting Leadership in MLPerf Tests appeared first on NVIDIA Blog.
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Amazon Rekognition Custom Labels is a fully managed computer vision service that allows developers to build custom models to classify and identify objects in images that are specific and unique to your business. Rekognition Custom Labels doesn’t require you to have any prior computer vision expertise. You can get started by simply uploading tens of […]
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A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.
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Perceiver and PerceiverIO (https://arxiv.org/abs/2107.14795) appear to offer significantly improved FLOP efficiency, but new LLMs (including Deepmind's own Gopher) don't use it.
What gives? Is it still too new, or is the Perceiver architecture not appropriate for LLMs?
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Saw this posted on Schmidhuber's Twitter:
Meta-Learning Machines in a Single Lifelong Trial: lecture video (24 min) presented at meta-learning workshops at ICML 2020 and NeurIPS 2021. URL of talk: https://youtu.be/2GgGVdkq2bU
Abstract
The most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta-learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987. In the past decade, it has become a driver of many other people's research as well. Here I summarize our work starting in 1994 on meta-reinforcement learning with self-modifying policies in a single lifelong trial, and - since 2003 - mathematically optimal meta-learning through the self-referential Gödel Machine. This talk was previously presented at meta-learning workshops at ICML 2020 and NeurIPS 2021. Many additional publications on meta-learning can be found at https://people.idsia.ch/~juergen/metalearning.html
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Autoregressive models like GPT-2 do fairly well in text generation. Is it possible to do the same for graph data? A transformer based model Graphormer has recently shown its effectiveness in graph representation learning. Is there any way I can train Graphormer or any other model to generate graphs from an initial graph context?
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Sponsored Post Join the UK’s most forward-thinking technologists and business professionals this June in a celebration of emerging technology. Machine […]
The post Win tickets to The AI Summit London 2022 appeared first on Machine Learning Mastery.
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MIT researchers design a robot that has a trick or two up its sleeve.
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The built-in Amazon SageMaker XGBoost algorithm provides a managed container to run the popular XGBoost machine learning (ML) framework, with added convenience of supporting advanced training or inference features like distributed training, dataset sharding for large-scale datasets, A/B model testing, or multi-model inference endpoints. You can also extend this powerful algorithm to accommodate different requirements. […]
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Research over the past few years has shown that machine learning (ML) models are vulnerable to adversarial inputs, where an adversary can craft inputs to strategically alter the model’s output (in image classification, speech recognition, or fraud detection). For example, imagine you have deployed a model that identifies your employees based on images of their […]
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Square/Enix presents the fictional city of Midgar in Final Fantasy VII Remake at a filmic level of detail. Epic’s Fortnite bathes its environments in ray-traced sunlight, simulating how light bounces in the real world. And artists at Lucasfilm revolutionized virtual production techniques in The Mandalorian, using synchronized NVIDIA RTX GPUs to drive pixels on LED Read article >
The post Unreal Engine and NVIDIA: From One Generation to the Next appeared first on NVIDIA Blog.
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A dozen companies today received NVIDIA’s highest award for partners, recognizing their impact on AI education and adoption across such industries as education, federal, healthcare and technology. The winners of the 2021 NPN Americas Partner of the Year Awards have created a profound impact on AI by helping customers meet the demands of recommender systems, Read article >
The post Green Teams Achieve the Dream: NVIDIA Announces NPN Americas Partners of the Year appeared first on NVIDIA Blog.
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Given a NN class, is there something specific we need to care of when converting *args and **kwargs to a canonical kwarg representation? I ask this because in this code from Google (https://github.com/google-research/google-research/blob/c56b47713b08c95ad427d5f93ee0dbb9ad008964/social_rl/multiagent_tfagents/joint_attention/attention_networks.py#L557) they use a TFDecorator-aware replacement for inspect.getcallargs, instead of using getcallargs directly. So my questions are:
- Is it possible to use inspect.getcallargs to convert *args and **kwargs to a canonical kwarg representation?
- If no, is there an equivalent in PyTorch? I couldn't find any, so I was wondering how people go about that.
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📑 The Random R-GCN code has just been released!
📝 With just a few lines of code, you can now create embeddings of entities in a Knowledge Graph.
Minimal example on how to create embeddings with RR-GCN
💡 RR-GCN does not require training and is competitive to fully trained R-GCNs.
👉 https://github.com/predict-idlab/RR-GCN
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As more organizations move to machine learning (ML) to drive deeper insights, two key stumbling blocks they run into are labeling and lifecycle management. Labeling is the identification of data and adding labels to provide context so an ML model can learn from it. Labels might indicate a phrase in an audio file, a car […]
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Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra supports a variety of document formats, […]
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Primary focus will be to advance and promote technology, innovation, and entrepreneurship across the school.
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Posted by Sharan Narang and Aakanksha Chowdhery, Software Engineers, Google Research
In recent years, large neural networks trained for language understanding and generation have achieved impressive results across a wide range of tasks. GPT-3 first showed that large language models (LLMs) can be used for few-shot learning and can achieve impressive results without large-scale task-specific data collection or model parameter updating. More recent LLMs, such as GLaM, LaMDA, Gopher, and Megatron-Turing NLG, achieved state-of-the-art few-shot results on many tasks by scaling model size, using sparsely activated modules, and training on larger datasets from more diverse sources. Yet much work remains in understanding the capabilities that emerge with few-shot learning as we push the limits of …
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Pekka Varis’s artistry has come a long way from his early days as a self-styled “punk activist” who spray painted during the “old school days of hip hop in Finland.”
The post Meet the Omnivore: Videographer Makes Digital Walls, Virtual Homes Pop With NVIDIA Omniverse appeared first on NVIDIA Blog.
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https://www.lesswrong.com/posts/midXmMb2Xg37F2Kgn/new-scaling-laws-for-large-language-models
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For the MIT Schwarzman College of Computing dean, bringing disciplines together is the best way to address challenges and opportunities posed by rapid advancements in computing.
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I am looking at this code from Google (https://github.com/google-research/google-research/blob/master/social_rl/multiagent_tfagents/joint_attention/attention_networks.py).
At line 639, the LSTM is called. The first two inputs are the state and the network state, but I don't understand what the latter is.
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